{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d4b1cbb2",
   "metadata": {},
   "source": [
    "## Count word frequency for each type of election fraud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e59e7d86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Region</th>\n",
       "      <th>Monitor</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Preprocessed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Niger</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Francophonie</td>\n",
       "      <td>1995</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>respons request nigerian author twice request ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Niger</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Francophonie</td>\n",
       "      <td>1995</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>part mission observ la francophoni work consul...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Niger</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Francophonie</td>\n",
       "      <td>1995</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>poll date poll date initi set decemb postpon t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Niger</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Francophonie</td>\n",
       "      <td>1995</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>nation commiss elect note decemb recept noncon...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Niger</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Francophonie</td>\n",
       "      <td>1995</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>access media elect campaign access media seem ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Niger</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Francophonie</td>\n",
       "      <td>1995</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>list elector establish list particularli diffi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Niger</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Francophonie</td>\n",
       "      <td>1995</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>nation elect commiss nation elect commiss orga...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Niger</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Francophonie</td>\n",
       "      <td>1995</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>establish poll station interlocutor mission me...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Niger</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Francophonie</td>\n",
       "      <td>1995</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>secur condit elector campaign seydou dan jouma...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Niger</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Francophonie</td>\n",
       "      <td>1995</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>elector campaign proceed satisfactori manner o...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Country  ...                                       Preprocessed\n",
       "0   Niger  ...  respons request nigerian author twice request ...\n",
       "1   Niger  ...  part mission observ la francophoni work consul...\n",
       "2   Niger  ...  poll date poll date initi set decemb postpon t...\n",
       "3   Niger  ...  nation commiss elect note decemb recept noncon...\n",
       "4   Niger  ...  access media elect campaign access media seem ...\n",
       "5   Niger  ...  list elector establish list particularli diffi...\n",
       "6   Niger  ...  nation elect commiss nation elect commiss orga...\n",
       "7   Niger  ...  establish poll station interlocutor mission me...\n",
       "8   Niger  ...  secur condit elector campaign seydou dan jouma...\n",
       "9   Niger  ...  elector campaign proceed satisfactori manner o...\n",
       "\n",
       "[10 rows x 7 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load a preprocessed corpus\n",
    "import pandas as pd\n",
    "corpus_path = 'preprocessed_corpus_stemmed.csv'\n",
    "\n",
    "df_corpus = pd.read_csv(corpus_path, na_values='NULL', index_col=0)\n",
    "df_corpus.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b417f927",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(92927, 7)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_corpus.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0fd83a33",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define counting functions\n",
    "import re\n",
    "\n",
    "def count_keys(doc, key):\n",
    "    return len(re.findall(key, doc))\n",
    "\n",
    "compute_size = lambda doc: len(doc.split())\n",
    "\n",
    "def compute_freq(docs, key):\n",
    "    count = docs.apply(count_keys, key=key).sum()\n",
    "    size = docs.apply(compute_size).sum()\n",
    "    return count, size, float(count) / float(size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1fe372bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'(station|poll|ballot|box)'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Specify a list of matching tokens(stems) for each type of fraud\n",
    "preelection = ['campaign', 'media', 'registr', 'candid'] # pre-election\n",
    "electionday = ['station', 'poll', 'ballot', 'box'] # election day\n",
    "postelection = ['disput', 'result', 'court', 'complaint'] # post-election\n",
    "structural =  ['suffrag', 'jurisdict', 'elector', 'law'] # structural\n",
    "\n",
    "matching_pre = \"({})\".format('|'.join(preelection))\n",
    "matching_eday = \"({})\".format('|'.join(electionday))\n",
    "matching_post = \"({})\".format('|'.join(postelection))\n",
    "matching_str = \"({})\".format('|'.join(structural))\n",
    "matching_eday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b9d7111d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pre-election...done\n",
      "election day...done\n",
      "post-election...done\n",
      "structural...done\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pre-election</th>\n",
       "      <th>election day</th>\n",
       "      <th>post-election</th>\n",
       "      <th>structural</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1995-1999 (n=6258)</th>\n",
       "      <td>0.015709</td>\n",
       "      <td>0.019843</td>\n",
       "      <td>0.008080</td>\n",
       "      <td>0.014870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-2004 (n=14916)</th>\n",
       "      <td>0.019723</td>\n",
       "      <td>0.017761</td>\n",
       "      <td>0.009489</td>\n",
       "      <td>0.015340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-2009 (n=21689)</th>\n",
       "      <td>0.021672</td>\n",
       "      <td>0.017664</td>\n",
       "      <td>0.010720</td>\n",
       "      <td>0.015752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-2014 (n=24465)</th>\n",
       "      <td>0.025518</td>\n",
       "      <td>0.016219</td>\n",
       "      <td>0.011660</td>\n",
       "      <td>0.018041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-2019 (n=25599)</th>\n",
       "      <td>0.026649</td>\n",
       "      <td>0.012202</td>\n",
       "      <td>0.010859</td>\n",
       "      <td>0.015973</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     pre-election  election day  post-election  structural\n",
       "1995-1999 (n=6258)       0.015709      0.019843       0.008080    0.014870\n",
       "2000-2004 (n=14916)      0.019723      0.017761       0.009489    0.015340\n",
       "2005-2009 (n=21689)      0.021672      0.017664       0.010720    0.015752\n",
       "2010-2014 (n=24465)      0.025518      0.016219       0.011660    0.018041\n",
       "2015-2019 (n=25599)      0.026649      0.012202       0.010859    0.015973"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute word frequencies for 5-year periods from 1995-2019\n",
    "keys = [matching_pre, matching_eday, matching_post, matching_str,]\n",
    "cols = ['pre-election', 'election day', 'post-election', 'structural',]\n",
    "\n",
    "start = 1995\n",
    "end = 2020\n",
    "interval = 5\n",
    "#entire_label = 'Entire'\n",
    "\n",
    "years = list(range(start, end, interval))\n",
    "labels = []\n",
    "for year in years:\n",
    "    start_year = year\n",
    "    end_year = year + interval\n",
    "    filt = (df_corpus.Year >= start_year) & (df_corpus.Year < end_year)\n",
    "    num = df_corpus[filt].Preprocessed.shape[0]\n",
    "    label = str(start_year) + '-' + str(end_year-1) + ' (n=%i)' % num\n",
    "    labels.append(label)\n",
    "\n",
    "df_tokenfreqs_year = pd.DataFrame()\n",
    "for i, key in enumerate(keys):\n",
    "    col_label = cols[i]\n",
    "    freqs = []\n",
    "    print(\"%s...\" % col_label, end=\"\")\n",
    "    for year in years:\n",
    "        start_year = year\n",
    "        end_year = year + interval\n",
    "        filt = (df_corpus.Year >= start_year) & (df_corpus.Year < end_year)\n",
    "        docs = df_corpus[filt].Preprocessed.astype('str')\n",
    "        #freq = compute_freq(docs, key)[2] * 1000\n",
    "        freq = compute_freq(docs, key)[2]\n",
    "        freqs.append(freq)\n",
    "    df_tokenfreqs_year[col_label] = freqs\n",
    "    print(\"done\")\n",
    "df_tokenfreqs_year.index = labels\n",
    "df_tokenfreqs_year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b4243fc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preparation for plotting\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "%matplotlib inline\n",
    "mpl.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d6cb5ed4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pre-election</th>\n",
       "      <th>election day</th>\n",
       "      <th>post-election</th>\n",
       "      <th>structural</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.021854</td>\n",
       "      <td>0.016738</td>\n",
       "      <td>0.010162</td>\n",
       "      <td>0.015995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.004437</td>\n",
       "      <td>0.002846</td>\n",
       "      <td>0.001399</td>\n",
       "      <td>0.001218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.015709</td>\n",
       "      <td>0.012202</td>\n",
       "      <td>0.008080</td>\n",
       "      <td>0.014870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.019723</td>\n",
       "      <td>0.016219</td>\n",
       "      <td>0.009489</td>\n",
       "      <td>0.015340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.021672</td>\n",
       "      <td>0.017664</td>\n",
       "      <td>0.010720</td>\n",
       "      <td>0.015752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.025518</td>\n",
       "      <td>0.017761</td>\n",
       "      <td>0.010859</td>\n",
       "      <td>0.015973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.026649</td>\n",
       "      <td>0.019843</td>\n",
       "      <td>0.011660</td>\n",
       "      <td>0.018041</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       pre-election  election day  post-election  structural\n",
       "count      5.000000      5.000000       5.000000    5.000000\n",
       "mean       0.021854      0.016738       0.010162    0.015995\n",
       "std        0.004437      0.002846       0.001399    0.001218\n",
       "min        0.015709      0.012202       0.008080    0.014870\n",
       "25%        0.019723      0.016219       0.009489    0.015340\n",
       "50%        0.021672      0.017664       0.010720    0.015752\n",
       "75%        0.025518      0.017761       0.010859    0.015973\n",
       "max        0.026649      0.019843       0.011660    0.018041"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting\n",
    "labels = []\n",
    "for year in years:\n",
    "    start_year = year\n",
    "    end_year = year + interval\n",
    "    label = str(start_year) + '-' + str(end_year-1)\n",
    "    labels.append(label)\n",
    "\n",
    "df_tokenfreqs_year.plot(figsize=(9, 6), logy=False)\n",
    "plt.legend(labels=cols, fontsize=14)\n",
    "plt.xticks(np.array(range(len(labels))), labels)\n",
    "#plt.title('Token frequencies (per 1000 words)')\n",
    "plt.title('Token frequencies')\n",
    "df_tokenfreqs_year.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1eff33d6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
